Conference paper
Estimation of shape model parameters for 3D surfaces
Statistical shape models are widely used as a compact way of representing shape variation. Fitting a shape model to unseen data enables characterizing the data in terms of the model parameters. In this paper a Gauss-Newton optimization scheme is proposed to estimate shape model parameters of 3D surfaces using distance maps, which enables the estimation of model parameters without the requirement of point correspondence.
For applications with acquisition limitations such as speed and cost, this formulation enables the fitting of a statistical shape model to arbitrarily sampled data. The method is applied to a database of 3D surfaces from a section of the porcine pelvic bone extracted from 33 CT scans. A leave-one-out validation shows that the parameters of the first 3 modes of the shape model can be predicted with a mean difference within [-0.01,0.02] from the true mean, with a standard deviation less than 0.34.
| Language: | English |
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| Publisher: | IEEE |
| Year: | 2008 |
| Pages: | 624-627 |
| Proceedings: | 2008 IEEE International Symposium on Biomedical Imaging |
| ISBN: | 1424420024 , 9781424420025 , 1424420032 and 9781424420032 |
| Types: | Conference paper |
| DOI: | 10.1109/ISBI.2008.4541073 |
| ORCIDs: | Ersbøll, Bjarne Kjær |
Biomedical image processing Image registration Image shape analysis Optimization methods X-ray tomography
3D surfaces CT scans Costs Data mining Databases Gauss-Newton optimization scheme Least squares methods Newton method Parameter estimation Pelvic bones Recursive estimation Shape Surface fitting biomedical image processing bone computerised tomography distance maps image registration image shape analysis medical image processing optimisation optimization methods porcine pelvic bone shape model parameter estimation statistical shape model
